Logs to the Rescue: Creating meaningful representations from log files for Anomaly Detection

This thesis offers a comprehensive exploration of log-based anomaly detection within the domain of cybersecurity incident response. The research describes a different approach and explores relevant log features for language model training, experimentation with different language models and training methodologies, and the investigation of the...

Data Model for Computer Vision Explainability, Fairness, and Robustness

In recent years, there has been a growing interest among researchers in the explainability, fairness, and robustness of Computer Vision models. While studies have explored the usability of these models for end users, limited research has delved into the challenges and requirements faced by researchers investigating these requirements. This study...

A System for Model Diagnosis centered around Human Computation

Machine learning (ML) systems for computer vision applications are widely deployed in decision-making contexts, including high-stakes domains such as autonomous driving and medical diagnosis. While largely accelerating the decision-making process, those systems have been found to suffer from a severe issue of reliability, i.e., they can easily...

Passive screen-camera communication

The advancement of wireless communication technologies has transformed how we exchange information in our daily lives. However, the increasing demand for wireless communication faces challenges due to limited radio wave bandwidth availability. In this context, visible light communication has emerged as a promising alternative, utilizing visible...

Compressing code generation language models on CPUs: Using Group Lasso pruning and post-training quantization

Code generation models have become more popular recently, due to the fact that they assist developers in writing code in a more productive manner. While these large models deliver impressive performance, they require significant computational resources and memory, making them difficult to deploy and expensive to train. Additionally, their large...

Distil-CodeGPT, Distilling Code-Generation Models for Local Use

The application of large language models (LLMs) for programming tasks, such as automatic code completion, has seen a significant upswing in recent years. However, due to their computational demands, they have to operate on servers. This both requires users to have a steady internet connection and raises potential privacy concerns. Therefore,...

Leveraging Efficient Transformer Quantization for CodeGPT: A Post-Training Analysis

The significant advancements in large language models have enabled their use in various applications, such as in code auto-completion. However, the deployment of such models often encounters challenges due to their large size and prohibitive running costs. In this research, we investigate the effectiveness of post-training quantization...

CodeGPT on XTC: Compressing a CodeGPT Model Using Hybrid Layer Reduction and Extreme Quantisation through Knowledge Distillation

Large language models are powerful because of their state-of-the-art language processing abilities. But, they come at the cost of being extremely resource-intensive, and are steadily growing in size. As a result, compressing such models for resource- constrained devices is an active and promising re- search area. In spite of their current...

Analyzing the Criticality of NPM Packages Through a Time-Dependent Dependency Graph

In (open-source) development, developers routinely rely on other libraries to improve their coding efficiency by reusing code. This reliance on other packages could cause issues when critical dependencies have suddenly have a vulnerability introduced to them. This work analyzes the criticality for NPM. To get an accurate picture of what the most...

Analyzing the effect of introducing time as a component in Python dependency graphs

The use of open-source packages is a common practice among developers. It decreases the development time and improves maintainability. But adding a dependency to a project comes with inherit risks such as introducing vulnerabilities. A few solutions that help visualize all of the dependencies of a project exist already. However, none provide the...

Analyzing the Criticality of Apache Maven Packages Through a Temporal Dependency Graph

Developers rely on different software to improve their efficiency as to reuse parts of code and be able to maintain it with ease, which is why open source software libraries have gained much pop- ularity over the past years. This paper analyzes what are the most used packages from Apache Maven, which is a build automation tool used primarily for...

Using a Time Dependency Graph to find the most widely used Debian package

The main principle of Open Source development is that developers can reuse different libraries over and over again to make their lives easier. That is why this practice has gained a lot of popularity. However, libraries usually depend on other already existing pieces of code. This means that whenever some small piece of code fails, the whole...

Implications on Learning Outcomes and Eye-strain of using Telepresence Robots as a new Distance Learning Medium

Distance learning brings all sorts of advantages. The ability to follow lectures at home can save people transportation costs and time. Teaching through videoconferencing software such as Zoom is one of the methods to learn remotely. To explore new and better teaching methods, the possibility of using a telepresence robot is investigated. To...

An Epistemic and Psychological Comparison Between Holograms and Zoom as Educational Media

Deficiencies in online communication software inspired the HoloLearn project that proposes to aid hybrid education via a hologram. Yet the epistemic and psychological effects of this medium remain largely uncharted. To help integrate the hologram into a hybrid education system, the present study conducted experiments which explored the nature of...

Effects of a holographic teacher projection on the engagement with the learning materials

This study examines the effects of holograms when used for online learning. The research question is: "How does a holographic teacher projection affect engagement of the students with the learning material?". The engagement of students was compared in different lectures. The base lecture was a zoom lecture. The holographic lectures were a VR...

Studying the Effects of Educative Holographic Projections in VR Environment

This study aims to examine the implications of bringing holographic projections – based on the TU Delft’s project ”HoloLearn” – into VR environment. With the spiked interest in remote communication caused by COVID-19, the demand for more immersive virtual conference software grows. And as Virtual Reality as a product becomes more affordable and...

User/Query-wise Metric Bounding in Learning to Rank

Learning to Rank is the application of Machine Learning in order to create and optimize ranking functions. Most Learning to Rank methods follow a listwise approach and optimize a listwise loss function which closely resembles the same metric used in the evaluation. Popular listwise loss functions such as nDCG, AP and nRBP do not have consistent...